This paper presents a deep learning-based method to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement used in the diagnosis and treatment of hip problems, but manual measurement can be time-consuming and prone to inter-observer variability.
The key highlights of the proposed approach are:
The method uses a U-Net architecture to learn features from X-ray images and predict the CCD angle. The U-Net is trained to predict heatmaps for the femur neck and shaft centerlines, which are then used to calculate the CCD angle.
The authors evaluated the method on a dataset of 201 hip X-ray images and achieved a mean absolute error of 4.3 degrees on the left femur and 4.9 degrees on the right femur, demonstrating high accuracy.
The authors also developed a prototype user interface that allows users to interact with the predictions, including the ability to edit the predicted lines and view the calculated CCD angle. The interface also supports voice control, which is important for the sterile operating room environment.
The user study conducted with the prototype showed high usability, with SUS scores between 80-90%, indicating the potential for the proposed method to be integrated into clinical workflows.
The results suggest that the deep learning-based approach has the potential to provide a more efficient and accurate technique for predicting the femur CCD angle, which could have significant implications for the diagnosis and management of hip problems.
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by Deepak Bhati... at arxiv.org 04-29-2024
https://arxiv.org/pdf/2404.17083.pdfDeeper Inquiries